Application of Acoustic Emission and Baseline‐Based Approach for Early Fatigue‐Damage Detection

Monitoring fatigue damage in mechanical connections is essential for maintaining the safety and structural integrity of offshore wind turbines (OWTs), particularly during the early stage of crack initiation. Recently, the C1 wedge connection (C1‐WC) has emerged as a promising innovation for use in OWTs. Acoustic emission (AE) monitoring is a widely used real‐time technique for detecting fatigue cracks. The space limitations of the lower segment holes in the C1‐WC presents challenges for detecting surface cracks with conventional AE sensors. Thin Piezoelectric Wafer Active Sensors (PWAS), while small and lightweight, face limitations due to their poor signal‐to‐noise ratio. In this study, we propose a baseline‐based approach to enhance the effectiveness of PWAS for accurate AE monitoring in confined spaces. A benchmark model correlating the damage state of specimens is created by breaking pencil leads. Multivariate feature vectors are extracted and then mapped to the Mahalanobis distance for damage identification. The proposed method is validated through testing on compact specimens and C1‐WC specimens. To enhance the AE detection results, supplementary monitoring techniques, including digital image correlation, crack propagation gauges, and distributed optical fiber sensors, are employed. The experimental setup, signal acquisition, and detection efficiency of these techniques are briefly outlined. This study demonstrates that the proposed approach is highly effective in detecting early damage in C1‐WC specimens using AE monitoring with PWAS.